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Gaussian process internal model control

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  • Gregor Gregorčič
  • Gordon Lightbody

Abstract

Nonlinear modelling approaches such as neural networks, fuzzy models and multiple model networks have been proposed for model based control, to improve the poor transient response of adaptive control techniques. The quality of control is known to be strongly related to the accuracy of the model which represents the process. A Bayesian Gaussian process (GP) approach provides an analytic prediction of the model uncertainty, which makes the GP model an ideal candidate for model based control strategies. This article extends the use of the GP model for nonlinear internal model control. The invertibility of the GP model is discussed and the use of predicted variance is illustrated on a simulated example.

Suggested Citation

  • Gregor Gregorčič & Gordon Lightbody, 2012. "Gaussian process internal model control," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(11), pages 2079-2094.
  • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:11:p:2079-2094
    DOI: 10.1080/00207721.2011.564326
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    Cited by:

    1. Ping Chen & Jing Yang & Linyuan Li, 2015. "Synthetic detection of change point and outliers in bilinear time series models," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(2), pages 284-293, January.

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